Third, we show that, at least on some hard SMAC maps, the currently best performance is achieved by an algorithm that does not require the learning of neither joint nor centralised value functions, and does not exploit central state information in any other way. This implies that the fundamental obstacles to independent learning methods posed by certain pathological matrix games [1]} are not present in such SMAC tasks. A possible explanation is that the sequential nature of SMAC tasks allows the decomposition of tricky simultaneous coordination tasks into a temporal sequence of easier coordination tasks.
The fact that the common learning pathology of relative overgeneralisation does not seem to be present in these SMAC tasks (see Fig. REF  in the Appendix for an empirical evidence that IPPO – like many value factorisation algorithms – is in principle prone to relative overgeneralisation).
